Overview

Dataset statistics

Number of variables36
Number of observations2216
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory705.1 KiB
Average record size in memory325.8 B

Variable types

Numeric19
Categorical16
DateTime1

Alerts

z_cost_contact has constant value "3"Constant
z_revenue has constant value "11"Constant
year_birth is highly overall correlated with ageHigh correlation
income is highly overall correlated with mnt_wines and 11 other fieldsHigh correlation
mnt_wines is highly overall correlated with income and 11 other fieldsHigh correlation
mnt_fruits is highly overall correlated with income and 9 other fieldsHigh correlation
mnt_meat_products is highly overall correlated with income and 10 other fieldsHigh correlation
mnt_fish_products is highly overall correlated with income and 9 other fieldsHigh correlation
mnt_sweet_products is highly overall correlated with income and 9 other fieldsHigh correlation
mnt_gold_prods is highly overall correlated with income and 10 other fieldsHigh correlation
num_web_purchases is highly overall correlated with income and 7 other fieldsHigh correlation
num_catalog_purchases is highly overall correlated with income and 11 other fieldsHigh correlation
num_store_purchases is highly overall correlated with income and 10 other fieldsHigh correlation
num_web_visits_month is highly overall correlated with income and 2 other fieldsHigh correlation
age is highly overall correlated with year_birthHigh correlation
income_per_member is highly overall correlated with income and 12 other fieldsHigh correlation
total_spent is highly overall correlated with income and 11 other fieldsHigh correlation
kidhome is highly overall correlated with n_family_members and 1 other fieldsHigh correlation
teenhome is highly overall correlated with n_sonsHigh correlation
accepted_cmp3 is highly overall correlated with total_acceptedHigh correlation
accepted_cmp4 is highly overall correlated with total_acceptedHigh correlation
accepted_cmp5 is highly overall correlated with mnt_wines and 2 other fieldsHigh correlation
accepted_cmp1 is highly overall correlated with total_acceptedHigh correlation
accepted_cmp2 is highly overall correlated with total_acceptedHigh correlation
n_family_members is highly overall correlated with income_per_member and 2 other fieldsHigh correlation
n_sons is highly overall correlated with kidhome and 2 other fieldsHigh correlation
total_accepted is highly overall correlated with accepted_cmp3 and 4 other fieldsHigh correlation
accepted_cmp3 is highly imbalanced (62.1%)Imbalance
accepted_cmp4 is highly imbalanced (61.9%)Imbalance
accepted_cmp5 is highly imbalanced (62.3%)Imbalance
accepted_cmp1 is highly imbalanced (65.7%)Imbalance
accepted_cmp2 is highly imbalanced (89.7%)Imbalance
complain is highly imbalanced (92.3%)Imbalance
total_accepted is highly imbalanced (57.1%)Imbalance
id has unique valuesUnique
recency has 28 (1.3%) zerosZeros
mnt_fruits has 395 (17.8%) zerosZeros
mnt_fish_products has 379 (17.1%) zerosZeros
mnt_sweet_products has 413 (18.6%) zerosZeros
mnt_gold_prods has 61 (2.8%) zerosZeros
num_deals_purchases has 44 (2.0%) zerosZeros
num_web_purchases has 48 (2.2%) zerosZeros
num_catalog_purchases has 576 (26.0%) zerosZeros

Reproduction

Analysis started2023-02-13 15:24:31.645026
Analysis finished2023-02-13 15:25:56.294271
Duration1 minute and 24.65 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct2216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5588.3533
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:25:56.410751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile572.75
Q12814.75
median5458.5
Q38421.75
95-th percentile10676.5
Maximum11191
Range11191
Interquartile range (IQR)5607

Descriptive statistics

Standard deviation3249.3763
Coefficient of variation (CV)0.58145505
Kurtosis-1.1896767
Mean5588.3533
Median Absolute Deviation (MAD)2791
Skewness0.040459216
Sum12383791
Variance10558446
MonotonicityNot monotonic
2023-02-13T12:25:56.610457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5524 1
 
< 0.1%
6885 1
 
< 0.1%
3478 1
 
< 0.1%
7494 1
 
< 0.1%
1763 1
 
< 0.1%
7250 1
 
< 0.1%
2005 1
 
< 0.1%
10770 1
 
< 0.1%
2072 1
 
< 0.1%
3643 1
 
< 0.1%
Other values (2206) 2206
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11176 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%

year_birth
Real number (ℝ)

Distinct59
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8204
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:25:56.810633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.985554
Coefficient of variation (CV)0.0060876828
Kurtosis0.73467044
Mean1968.8204
Median Absolute Deviation (MAD)9
Skewness-0.35366147
Sum4362906
Variance143.65351
MonotonicityNot monotonic
2023-02-13T12:25:57.010906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 86
 
3.9%
1975 83
 
3.7%
1972 78
 
3.5%
1978 76
 
3.4%
1970 75
 
3.4%
1965 74
 
3.3%
1973 72
 
3.2%
1969 70
 
3.2%
1974 69
 
3.1%
Other values (49) 1444
65.2%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1899 1
 
< 0.1%
1900 1
 
< 0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 6
 
0.3%
1944 7
0.3%
1945 8
0.4%
1946 16
0.7%
1947 16
0.7%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 29
1.3%
1988 29
1.3%
1987 27
1.2%

education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Graduation
1116 
PhD
481 
Master
365 
2n Cycle
200 
Basic
 
54

Length

Max length10
Median length10
Mean length7.5194043
Min length3

Characters and Unicode

Total characters16663
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation 1116
50.4%
PhD 481
21.7%
Master 365
 
16.5%
2n Cycle 200
 
9.0%
Basic 54
 
2.4%

Length

2023-02-13T12:25:57.295977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:25:57.612130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1116
46.2%
phd 481
19.9%
master 365
 
15.1%
2n 200
 
8.3%
cycle 200
 
8.3%
basic 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2651
15.9%
r 1481
8.9%
t 1481
8.9%
n 1316
 
7.9%
i 1170
 
7.0%
G 1116
 
6.7%
d 1116
 
6.7%
u 1116
 
6.7%
o 1116
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13566
81.4%
Uppercase Letter 2697
 
16.2%
Decimal Number 200
 
1.2%
Space Separator 200
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2651
19.5%
r 1481
10.9%
t 1481
10.9%
n 1316
9.7%
i 1170
8.6%
d 1116
8.2%
u 1116
8.2%
o 1116
8.2%
e 565
 
4.2%
h 481
 
3.5%
Other values (4) 1073
7.9%
Uppercase Letter
ValueCountFrequency (%)
G 1116
41.4%
D 481
17.8%
P 481
17.8%
M 365
 
13.5%
C 200
 
7.4%
B 54
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 200
100.0%
Space Separator
ValueCountFrequency (%)
200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16263
97.6%
Common 400
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2651
16.3%
r 1481
9.1%
t 1481
9.1%
n 1316
8.1%
i 1170
 
7.2%
G 1116
 
6.9%
d 1116
 
6.9%
u 1116
 
6.9%
o 1116
 
6.9%
e 565
 
3.5%
Other values (10) 3135
19.3%
Common
ValueCountFrequency (%)
2 200
50.0%
200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2651
15.9%
r 1481
8.9%
t 1481
8.9%
n 1316
 
7.9%
i 1170
 
7.0%
G 1116
 
6.7%
d 1116
 
6.7%
u 1116
 
6.7%
o 1116
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

marital_status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Married
857 
Together
573 
Single
471 
Divorced
232 
Widow
 
76
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.0758123
Min length4

Characters and Unicode

Total characters15680
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowTogether
4th rowTogether
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 857
38.7%
Together 573
25.9%
Single 471
21.3%
Divorced 232
 
10.5%
Widow 76
 
3.4%
Alone 3
 
0.1%
Absurd 2
 
0.1%
YOLO 2
 
0.1%

Length

2023-02-13T12:25:57.881706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:25:58.197798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
married 857
38.7%
together 573
25.9%
single 471
21.3%
divorced 232
 
10.5%
widow 76
 
3.4%
alone 3
 
0.1%
absurd 2
 
0.1%
yolo 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2709
17.3%
r 2521
16.1%
i 1636
10.4%
d 1167
7.4%
g 1044
 
6.7%
o 884
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 573
 
3.7%
t 573
 
3.7%
Other values (16) 2859
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13458
85.8%
Uppercase Letter 2222
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2709
20.1%
r 2521
18.7%
i 1636
12.2%
d 1167
8.7%
g 1044
 
7.8%
o 884
 
6.6%
a 857
 
6.4%
t 573
 
4.3%
h 573
 
4.3%
n 474
 
3.5%
Other values (7) 1020
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
M 857
38.6%
T 573
25.8%
S 471
21.2%
D 232
 
10.4%
W 76
 
3.4%
A 5
 
0.2%
O 4
 
0.2%
Y 2
 
0.1%
L 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 15680
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2709
17.3%
r 2521
16.1%
i 1636
10.4%
d 1167
7.4%
g 1044
 
6.7%
o 884
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 573
 
3.7%
t 573
 
3.7%
Other values (16) 2859
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2709
17.3%
r 2521
16.1%
i 1636
10.4%
d 1167
7.4%
g 1044
 
6.7%
o 884
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 573
 
3.7%
t 573
 
3.7%
Other values (16) 2859
18.2%

income
Real number (ℝ)

Distinct1974
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52247.251
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:25:58.445214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18985.5
Q135303
median51381.5
Q368522
95-th percentile84130
Maximum666666
Range664936
Interquartile range (IQR)33219

Descriptive statistics

Standard deviation25173.077
Coefficient of variation (CV)0.48180672
Kurtosis159.6367
Mean52247.251
Median Absolute Deviation (MAD)16557.5
Skewness6.7634874
Sum1.1577991 × 108
Variance6.3368379 × 108
MonotonicityNot monotonic
2023-02-13T12:25:58.646022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
37760 3
 
0.1%
83844 3
 
0.1%
63841 3
 
0.1%
18929 3
 
0.1%
47025 3
 
0.1%
34176 3
 
0.1%
48432 3
 
0.1%
39922 3
 
0.1%
Other values (1964) 2176
98.2%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

kidhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1283 
1
887 
2
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Length

2023-02-13T12:25:59.517465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:25:59.717336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1283
57.9%
1 887
40.0%
2 46
 
2.1%

teenhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1147 
1
1018 
2
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Length

2023-02-13T12:25:59.933541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:00.252328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1147
51.8%
1 1018
45.9%
2 51
 
2.3%
Distinct662
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
Minimum2012-07-30 00:00:00
Maximum2014-06-29 00:00:00
2023-02-13T12:26:00.526330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:26:00.803949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

recency
Real number (ℝ)

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.012635
Minimum0
Maximum99
Zeros28
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:01.067024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.948352
Coefficient of variation (CV)0.59063038
Kurtosis-1.1997769
Mean49.012635
Median Absolute Deviation (MAD)25
Skewness0.0016477067
Sum108612
Variance838.00706
MonotonicityNot monotonic
2023-02-13T12:26:01.311726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.4%
65 30
 
1.4%
71 29
 
1.3%
3 29
 
1.3%
29 29
 
1.3%
49 29
 
1.3%
Other values (90) 1908
86.1%
ValueCountFrequency (%)
0 28
1.3%
1 24
1.1%
2 28
1.3%
3 29
1.3%
4 26
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 23
1.0%
95 18
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.4%
91 18
0.8%
90 20
0.9%

mnt_wines
Real number (ℝ)

Distinct776
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.09161
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:01.695475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median174.5
Q3505
95-th percentile1000.25
Maximum1493
Range1493
Interquartile range (IQR)481

Descriptive statistics

Standard deviation337.32792
Coefficient of variation (CV)1.1056611
Kurtosis0.58274112
Mean305.09161
Median Absolute Deviation (MAD)165.5
Skewness1.1707201
Sum676083
Variance113790.13
MonotonicityNot monotonic
2023-02-13T12:26:02.048824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
1 37
 
1.7%
6 37
 
1.7%
5 37
 
1.7%
4 33
 
1.5%
8 30
 
1.4%
3 30
 
1.4%
9 28
 
1.3%
12 25
 
1.1%
14 24
 
1.1%
Other values (766) 1893
85.4%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.4%
4 33
1.5%
5 37
1.7%
6 37
1.7%
7 21
0.9%
8 30
1.4%
9 28
1.3%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

mnt_fruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.356047
Minimum0
Maximum199
Zeros395
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:02.565778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q333
95-th percentile122.25
Maximum199
Range199
Interquartile range (IQR)31

Descriptive statistics

Standard deviation39.793917
Coefficient of variation (CV)1.5098591
Kurtosis4.0540815
Mean26.356047
Median Absolute Deviation (MAD)8
Skewness2.1016575
Sum58405
Variance1583.5558
MonotonicityNot monotonic
2023-02-13T12:26:02.789224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 395
 
17.8%
1 158
 
7.1%
2 119
 
5.4%
3 114
 
5.1%
4 103
 
4.6%
7 67
 
3.0%
5 62
 
2.8%
6 62
 
2.8%
12 50
 
2.3%
8 48
 
2.2%
Other values (148) 1038
46.8%
ValueCountFrequency (%)
0 395
17.8%
1 158
 
7.1%
2 119
 
5.4%
3 114
 
5.1%
4 103
 
4.6%
5 62
 
2.8%
6 62
 
2.8%
7 67
 
3.0%
8 48
 
2.2%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

mnt_meat_products
Real number (ℝ)

Distinct554
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.99594
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:02.998314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median68
Q3232.25
95-th percentile687.5
Maximum1725
Range1725
Interquartile range (IQR)216.25

Descriptive statistics

Standard deviation224.28327
Coefficient of variation (CV)1.3430463
Kurtosis5.0554767
Mean166.99594
Median Absolute Deviation (MAD)60
Skewness2.0255768
Sum370063
Variance50302.986
MonotonicityNot monotonic
2023-02-13T12:26:03.221031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 50
 
2.3%
11 49
 
2.2%
8 45
 
2.0%
6 42
 
1.9%
10 40
 
1.8%
3 39
 
1.8%
9 37
 
1.7%
16 35
 
1.6%
12 34
 
1.5%
Other values (544) 1792
80.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.4%
3 39
1.8%
4 30
1.4%
5 50
2.3%
6 42
1.9%
7 53
2.4%
8 45
2.0%
9 37
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%
946 1
< 0.1%

mnt_fish_products
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.637635
Minimum0
Maximum259
Zeros379
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:03.483840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile169
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.752082
Coefficient of variation (CV)1.4547163
Kurtosis3.0764763
Mean37.637635
Median Absolute Deviation (MAD)12
Skewness1.916369
Sum83405
Variance2997.7905
MonotonicityNot monotonic
2023-02-13T12:26:03.737816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
 
17.1%
2 152
 
6.9%
3 128
 
5.8%
4 108
 
4.9%
6 81
 
3.7%
7 66
 
3.0%
8 58
 
2.6%
10 54
 
2.4%
13 48
 
2.2%
11 46
 
2.1%
Other values (172) 1096
49.5%
ValueCountFrequency (%)
0 379
17.1%
1 10
 
0.5%
2 152
6.9%
3 128
 
5.8%
4 108
 
4.9%
5 1
 
< 0.1%
6 81
 
3.7%
7 66
 
3.0%
8 58
 
2.6%
10 54
 
2.4%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

mnt_sweet_products
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct176
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.028881
Minimum0
Maximum262
Zeros413
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:03.969228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile125.25
Maximum262
Range262
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.072046
Coefficient of variation (CV)1.5195615
Kurtosis4.1061406
Mean27.028881
Median Absolute Deviation (MAD)8
Skewness2.1033276
Sum59896
Variance1686.9129
MonotonicityNot monotonic
2023-02-13T12:26:04.216671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
18.6%
1 161
 
7.3%
2 123
 
5.6%
3 101
 
4.6%
4 80
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
12 45
 
2.0%
Other values (166) 1052
47.5%
ValueCountFrequency (%)
0 413
18.6%
1 161
 
7.3%
2 123
 
5.6%
3 101
 
4.6%
4 80
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%
188 1
 
< 0.1%

mnt_gold_prods
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct212
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.965253
Minimum0
Maximum321
Zeros61
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:04.454137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24.5
Q356
95-th percentile165.25
Maximum321
Range321
Interquartile range (IQR)47

Descriptive statistics

Standard deviation51.815414
Coefficient of variation (CV)1.1785538
Kurtosis3.1563419
Mean43.965253
Median Absolute Deviation (MAD)18.5
Skewness1.8392309
Sum97427
Variance2684.8372
MonotonicityNot monotonic
2023-02-13T12:26:04.670347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 71
 
3.2%
4 69
 
3.1%
3 68
 
3.1%
12 63
 
2.8%
5 63
 
2.8%
2 62
 
2.8%
0 61
 
2.8%
6 55
 
2.5%
7 52
 
2.3%
10 49
 
2.2%
Other values (202) 1603
72.3%
ValueCountFrequency (%)
0 61
2.8%
1 71
3.2%
2 62
2.8%
3 68
3.1%
4 69
3.1%
5 63
2.8%
6 55
2.5%
7 52
2.3%
8 40
1.8%
9 43
1.9%
ValueCountFrequency (%)
321 1
 
< 0.1%
291 1
 
< 0.1%
262 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
245 1
 
< 0.1%
242 2
 
0.1%
241 6
0.3%

num_deals_purchases
Real number (ℝ)

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.323556
Minimum0
Maximum15
Zeros44
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:04.836881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9237156
Coefficient of variation (CV)0.82791879
Kurtosis8.9744901
Mean2.323556
Median Absolute Deviation (MAD)1
Skewness2.4152718
Sum5149
Variance3.7006819
MonotonicityNot monotonic
2023-02-13T12:26:04.970973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 960
43.3%
2 493
22.2%
3 293
 
13.2%
4 188
 
8.5%
5 94
 
4.2%
6 60
 
2.7%
0 44
 
2.0%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 23
 
1.0%
ValueCountFrequency (%)
0 44
 
2.0%
1 960
43.3%
2 493
22.2%
3 293
 
13.2%
4 188
 
8.5%
5 94
 
4.2%
6 60
 
2.7%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 3
 
0.1%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 39
1.8%
6 60
2.7%
5 94
4.2%

num_web_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0852888
Minimum0
Maximum27
Zeros48
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:05.117654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7409511
Coefficient of variation (CV)0.67093202
Kurtosis4.0721368
Mean4.0852888
Median Absolute Deviation (MAD)2
Skewness1.197037
Sum9053
Variance7.5128128
MonotonicityNot monotonic
2023-02-13T12:26:05.255776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 368
16.6%
1 348
15.7%
3 334
15.1%
4 277
12.5%
5 219
9.9%
6 201
9.1%
7 154
6.9%
8 102
 
4.6%
9 75
 
3.4%
0 48
 
2.2%
Other values (5) 90
 
4.1%
ValueCountFrequency (%)
0 48
 
2.2%
1 348
15.7%
2 368
16.6%
3 334
15.1%
4 277
12.5%
5 219
9.9%
6 201
9.1%
7 154
6.9%
8 102
 
4.6%
9 75
 
3.4%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.4%
8 102
4.6%
7 154
6.9%
6 201
9.1%
5 219
9.9%

num_catalog_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6710289
Minimum0
Maximum28
Zeros576
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:05.408162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9267336
Coefficient of variation (CV)1.0957327
Kurtosis8.0671262
Mean2.6710289
Median Absolute Deviation (MAD)2
Skewness1.8810751
Sum5919
Variance8.5657698
MonotonicityNot monotonic
2023-02-13T12:26:05.525186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 576
26.0%
1 492
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 128
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
10 47
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 576
26.0%
1 492
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 128
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.9%
10 47
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.6%
6 128
5.8%
5 137
6.2%
4 181
8.2%

num_store_purchases
Real number (ℝ)

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8009928
Minimum0
Maximum13
Zeros14
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:05.656821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2507848
Coefficient of variation (CV)0.56038422
Kurtosis-0.62646219
Mean5.8009928
Median Absolute Deviation (MAD)2
Skewness0.7018263
Sum12855
Variance10.567602
MonotonicityNot monotonic
2023-02-13T12:26:05.804000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 484
21.8%
4 319
14.4%
2 220
9.9%
5 211
9.5%
6 177
 
8.0%
8 147
 
6.6%
7 141
 
6.4%
10 124
 
5.6%
9 106
 
4.8%
12 104
 
4.7%
Other values (4) 183
 
8.3%
ValueCountFrequency (%)
0 14
 
0.6%
1 6
 
0.3%
2 220
9.9%
3 484
21.8%
4 319
14.4%
5 211
9.5%
6 177
 
8.0%
7 141
 
6.4%
8 147
 
6.6%
9 106
 
4.8%
ValueCountFrequency (%)
13 83
 
3.7%
12 104
 
4.7%
11 80
 
3.6%
10 124
 
5.6%
9 106
 
4.8%
8 147
6.6%
7 141
6.4%
6 177
8.0%
5 211
9.5%
4 319
14.4%

num_web_visits_month
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3190433
Minimum0
Maximum20
Zeros10
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:05.958415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4253585
Coefficient of variation (CV)0.45597646
Kurtosis1.8525767
Mean5.3190433
Median Absolute Deviation (MAD)2
Skewness0.21804305
Sum11787
Variance5.8823641
MonotonicityNot monotonic
2023-02-13T12:26:06.119728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 387
17.5%
8 340
15.3%
6 335
15.1%
5 279
12.6%
4 217
9.8%
3 203
9.2%
2 201
9.1%
1 150
 
6.8%
9 82
 
3.7%
0 10
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 10
 
0.5%
1 150
 
6.8%
2 201
9.1%
3 203
9.2%
4 217
9.8%
5 279
12.6%
6 335
15.1%
7 387
17.5%
8 340
15.3%
9 82
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 82
 
3.7%
8 340
15.3%
7 387
17.5%
6 335
15.1%

accepted_cmp3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2053 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Length

2023-02-13T12:26:06.273068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:06.437059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2053
92.6%
1 163
 
7.4%

accepted_cmp4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2052 
1
 
164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Length

2023-02-13T12:26:06.542656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:06.705571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2052
92.6%
1 164
 
7.4%

accepted_cmp5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2054 
1
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Length

2023-02-13T12:26:06.827949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:06.979437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2054
92.7%
1 162
 
7.3%

accepted_cmp1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2074 
1
 
142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Length

2023-02-13T12:26:07.106477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:07.260190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2074
93.6%
1 142
 
6.4%

accepted_cmp2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2186 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Length

2023-02-13T12:26:07.391726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:07.545073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2186
98.6%
1 30
 
1.4%

complain
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
2195 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Length

2023-02-13T12:26:07.660986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:07.830285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2195
99.1%
1 21
 
0.9%

z_cost_contact
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
3
2216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2216
100.0%

Length

2023-02-13T12:26:07.946344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:08.093215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2216
100.0%

Most occurring characters

ValueCountFrequency (%)
3 2216
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2216
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2216
100.0%

z_revenue
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
11
2216 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4432
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 2216
100.0%

Length

2023-02-13T12:26:08.263010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:08.447982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
11 2216
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4432
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4432
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4432
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4432
100.0%

age
Real number (ℝ)

Distinct59
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.179603
Minimum24
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:08.600792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile32
Q143
median50
Q361
95-th percentile70
Maximum127
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.985554
Coefficient of variation (CV)0.23418615
Kurtosis0.73467044
Mean51.179603
Median Absolute Deviation (MAD)9
Skewness0.35366147
Sum113414
Variance143.65351
MonotonicityNot monotonic
2023-02-13T12:26:08.795942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 89
 
4.0%
49 86
 
3.9%
45 83
 
3.7%
48 78
 
3.5%
42 76
 
3.4%
50 75
 
3.4%
55 74
 
3.3%
47 72
 
3.2%
51 70
 
3.2%
46 69
 
3.1%
Other values (49) 1444
65.2%
ValueCountFrequency (%)
24 2
 
0.1%
25 5
 
0.2%
26 3
 
0.1%
27 5
 
0.2%
28 13
0.6%
29 15
0.7%
30 18
0.8%
31 29
1.3%
32 29
1.3%
33 27
1.2%
ValueCountFrequency (%)
127 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
80 1
 
< 0.1%
79 1
 
< 0.1%
77 6
 
0.3%
76 7
0.3%
75 8
0.4%
74 16
0.7%
73 16
0.7%

n_family_members
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
3
880 
2
757 
4
296 
1
252 
5
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 880
39.7%
2 757
34.2%
4 296
 
13.4%
1 252
 
11.4%
5 31
 
1.4%

Length

2023-02-13T12:26:08.965284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:09.150047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 880
39.7%
2 757
34.2%
4 296
 
13.4%
1 252
 
11.4%
5 31
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3 880
39.7%
2 757
34.2%
4 296
 
13.4%
1 252
 
11.4%
5 31
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 880
39.7%
2 757
34.2%
4 296
 
13.4%
1 252
 
11.4%
5 31
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 880
39.7%
2 757
34.2%
4 296
 
13.4%
1 252
 
11.4%
5 31
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 880
39.7%
2 757
34.2%
4 296
 
13.4%
1 252
 
11.4%
5 31
 
1.4%

n_sons
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
1
1117 
0
633 
2
416 
3
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1117
50.4%
0 633
28.6%
2 416
 
18.8%
3 50
 
2.3%

Length

2023-02-13T12:26:09.435248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:09.752257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1117
50.4%
0 633
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 1117
50.4%
0 633
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1117
50.4%
0 633
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1117
50.4%
0 633
28.6%
2 416
 
18.8%
3 50
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1117
50.4%
0 633
28.6%
2 416
 
18.8%
3 50
 
2.3%

days_since_signup
Real number (ℝ)

Distinct662
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.52121
Minimum0
Maximum699
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:09.984099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1180
median355.5
Q3529
95-th percentile667
Maximum699
Range699
Interquartile range (IQR)349

Descriptive statistics

Standard deviation202.43467
Coefficient of variation (CV)0.57262383
Kurtosis-1.1998022
Mean353.52121
Median Absolute Deviation (MAD)174.5
Skewness-0.016922159
Sum783403
Variance40979.795
MonotonicityNot monotonic
2023-02-13T12:26:10.253848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
667 12
 
0.5%
655 11
 
0.5%
500 11
 
0.5%
48 11
 
0.5%
313 10
 
0.5%
38 10
 
0.5%
120 9
 
0.4%
98 9
 
0.4%
85 9
 
0.4%
543 9
 
0.4%
Other values (652) 2115
95.4%
ValueCountFrequency (%)
0 2
 
0.1%
1 3
0.1%
2 3
0.1%
3 4
0.2%
4 5
0.2%
5 2
 
0.1%
6 2
 
0.1%
7 5
0.2%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
699 1
 
< 0.1%
698 1
 
< 0.1%
697 4
0.2%
696 3
0.1%
695 5
0.2%
694 4
0.2%
693 1
 
< 0.1%
692 3
0.1%
691 4
0.2%
690 7
0.3%

income_per_member
Real number (ℝ)

Distinct1984
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24993.109
Minimum815.66667
Maximum222222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:10.516944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum815.66667
5-th percentile6798
Q112105
median18650.833
Q332003
95-th percentile74692.5
Maximum222222
Range221406.33
Interquartile range (IQR)19898

Descriptive statistics

Standard deviation19938.134
Coefficient of variation (CV)0.79774527
Kurtosis7.2809277
Mean24993.109
Median Absolute Deviation (MAD)7731.1667
Skewness2.1527044
Sum55384729
Variance3.9752919 × 108
MonotonicityNot monotonic
2023-02-13T12:26:10.755089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500 7
 
0.3%
3750 5
 
0.2%
11953.33333 4
 
0.2%
22481.66667 3
 
0.1%
12787 3
 
0.1%
24216 3
 
0.1%
13307.33333 3
 
0.1%
9345 3
 
0.1%
18880 3
 
0.1%
15366 3
 
0.1%
Other values (1974) 2179
98.3%
ValueCountFrequency (%)
815.6666667 1
 
< 0.1%
1005.75 1
 
< 0.1%
1412 1
 
< 0.1%
1476 1
 
< 0.1%
1730 1
 
< 0.1%
1751 1
 
< 0.1%
1768.333333 1
 
< 0.1%
2278.333333 1
 
< 0.1%
2381.333333 1
 
< 0.1%
2500 7
0.3%
ValueCountFrequency (%)
222222 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
102692 1
< 0.1%
101970 1
< 0.1%
98777 2
0.1%
96843 1
< 0.1%
95529 1
< 0.1%
95169 1
< 0.1%
93790 1
< 0.1%

total_spent
Real number (ℝ)

Distinct1047
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607.07536
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.6 KiB
2023-02-13T12:26:10.955745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median396.5
Q31048
95-th percentile1778.25
Maximum2525
Range2520
Interquartile range (IQR)979

Descriptive statistics

Standard deviation602.90048
Coefficient of variation (CV)0.99312295
Kurtosis-0.34653478
Mean607.07536
Median Absolute Deviation (MAD)353.5
Skewness0.8580548
Sum1345279
Variance363488.98
MonotonicityNot monotonic
2023-02-13T12:26:11.240523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 18
 
0.8%
46 18
 
0.8%
57 16
 
0.7%
55 15
 
0.7%
44 15
 
0.7%
38 14
 
0.6%
37 14
 
0.6%
43 14
 
0.6%
20 14
 
0.6%
48 14
 
0.6%
Other values (1037) 2064
93.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
8 4
 
0.2%
9 2
 
0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 10
0.5%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 2
0.1%
2283 1
< 0.1%
2279 1
< 0.1%

total_accepted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1757 
1
323 
2
 
81
3
 
44
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1757
79.3%
1 323
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Length

2023-02-13T12:26:11.434560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:11.603866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1757
79.3%
1 323
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1757
79.3%
1 323
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1757
79.3%
1 323
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1757
79.3%
1 323
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1757
79.3%
1 323
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
0
1883 
1
333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Length

2023-02-13T12:26:11.772398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T12:26:11.935148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1883
85.0%
1 333
 
15.0%

Interactions

2023-02-13T12:25:50.003848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:37.137826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:41.115896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:45.177192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:48.964971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:01.447374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:04.855065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:08.324287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:11.801856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:15.039462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:18.487448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:22.074257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:26.370874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:29.710168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:33.219049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:36.510016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:39.987969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:43.195725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:46.619860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:50.192972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:37.384964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:41.331664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:45.357868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:49.149574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:01.610236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:05.036195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:08.493742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:11.970803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:15.225044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:18.634638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:22.228033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:26.533702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:29.880976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:33.388040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:36.694964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:40.166424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:43.374136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:46.767005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:50.364492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:37.577007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:41.547427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:45.593809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:49.318860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:01.785224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:05.202517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:08.666284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:12.155831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:15.441248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:18.803784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:22.397014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:26.708786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:30.042379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:33.557191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:36.873116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:40.335770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:43.543483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:46.936016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:50.545135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:37.792935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:41.769800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:45.794517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:49.503779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:01.964373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:05.371916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:08.841314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:12.334239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:15.663563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:18.988809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:22.581694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:26.888318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:30.211694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:33.720412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:37.042474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:40.504892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:43.712651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:47.105325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:50.696346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:37.993100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:41.985987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:45.979573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:49.704483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:02.133714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:05.541095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:09.026341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:12.503147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:15.857610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:19.173732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:22.860392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:27.051216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:30.365426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:33.889806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:37.212020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:40.667702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:43.875457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:47.268128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:50.865668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:38.193841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:42.198917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:46.180325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:49.920678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:02.318934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:05.741261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:09.215414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:12.688151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:16.080798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:19.336322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:23.849645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:27.223724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:30.543739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:34.122247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:37.412724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:40.837014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:44.075975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:47.484328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:51.067178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:38.452984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:42.418694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:46.398968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:50.128163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:02.496288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:05.957513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:09.395984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:12.857061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:16.265245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:19.521303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:24.055010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:27.389626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:30.713044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:34.321848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:37.597698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:41.018617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:44.276216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:47.722665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:51.244508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:38.663244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:42.618958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:46.597024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:50.337193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:02.681304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:06.142123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:09.580989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:13.019678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:16.443756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:19.690666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:24.243547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:27.574156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:30.925504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:34.506910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:37.802686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:41.191422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:44.461298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:47.954123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:51.398321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:38.863447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:42.819212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:46.782038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:50.522100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:02.852350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:06.320554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:09.765620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:13.204655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:16.616983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:19.862336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-13T12:25:51.567631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-13T12:24:43.019739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-13T12:25:13.536524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-13T12:25:20.190984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:24.782492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:28.159736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:31.515826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:35.007356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:38.349504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:41.670475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:44.978834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:48.486645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:51.915389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:39.464942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:43.473704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:47.430468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:59.989820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:03.416329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:06.843971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:10.367350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:13.705556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:17.118988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:20.375545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:24.998729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:28.326286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:31.747128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:35.176297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:38.546596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:41.839466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:45.163814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:48.656028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:52.069084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:39.658816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:43.705534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:47.630719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:00.228151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:03.584292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:07.044558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:10.545806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:13.858809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:17.268024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:20.538410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:25.152457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:28.491611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:31.947681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:35.338589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:38.715978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:41.993104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:45.332884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:48.825308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:52.269421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:39.859421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:43.990398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:47.815654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:00.406654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:03.753164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:07.238327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:10.699486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:14.035902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:17.446534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:20.707847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:25.330868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:28.676531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:32.163510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:35.507986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:38.885123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:42.187204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:45.502222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:48.980758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:53.235609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:40.061121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:44.206592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:48.002887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:00.575955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:03.937984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:07.407731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:10.884240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:14.206804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:17.600238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:20.878455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:25.500205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:28.860731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:32.332867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:35.678685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:39.079249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:42.340957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:45.665103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:49.172600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:53.402995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:40.282451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:44.422549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:48.216486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:00.778348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:04.154272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:07.607074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:11.100048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:14.391890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:17.785526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:21.070062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:25.684421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:29.054820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:32.538733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:35.855025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:39.279896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:42.541572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:45.849719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:49.341926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:53.572379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:40.467022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:44.607617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:48.401393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:00.946113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:04.332247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:07.777116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:11.288627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:14.561001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:17.970538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:21.239563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:25.853729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:29.208578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:32.718225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:36.008758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:39.446804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:42.710686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:46.019105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:49.498185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:53.740471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:40.699083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:44.813657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:48.601639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:01.124606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:04.517940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:07.945961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:11.469625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:14.723806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:18.149048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:21.403497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:26.047915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:29.377966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:32.887237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:36.196068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:39.633917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:42.888718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:46.295613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:49.673995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:53.904320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:40.930866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:45.007862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:24:48.780030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:01.282030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:04.685698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:08.146282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:11.632483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:14.877579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:18.318102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:21.906590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:26.208545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:29.562968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:33.056287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:36.340687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:39.818927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:43.042419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:46.450875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T12:25:49.827782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-13T12:26:12.249356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
idyear_birthincomerecencymnt_winesmnt_fruitsmnt_meat_productsmnt_fish_productsmnt_sweet_productsmnt_gold_prodsnum_deals_purchasesnum_web_purchasesnum_catalog_purchasesnum_store_purchasesnum_web_visits_monthagedays_since_signupincome_per_membertotal_spenteducationmarital_statuskidhometeenhomeaccepted_cmp3accepted_cmp4accepted_cmp5accepted_cmp1accepted_cmp2complainn_family_membersn_sonstotal_acceptedresponse
id1.0000.0050.004-0.044-0.025-0.020-0.014-0.030-0.033-0.041-0.027-0.025-0.011-0.022-0.012-0.005-0.003-0.007-0.0250.0000.0000.0000.0000.0000.0000.0000.0450.0360.0000.0000.0000.0000.034
year_birth0.0051.000-0.217-0.017-0.235-0.026-0.113-0.0300.002-0.076-0.085-0.166-0.179-0.1670.134-1.0000.015-0.155-0.1590.1130.0910.2210.3070.0530.0520.0830.0530.0000.1380.1350.1820.0430.000
income0.004-0.2171.0000.0080.8300.5820.8170.5770.5670.506-0.1960.5730.7920.732-0.6440.217-0.0230.8630.8510.0540.0000.2860.1740.0000.1390.4490.3620.0670.0000.2290.3040.2170.192
recency-0.044-0.0170.0081.0000.0170.0250.0260.0130.0240.0170.008-0.0020.0290.004-0.0190.0170.026-0.0010.0190.0000.0260.0660.0500.0430.0000.0000.0000.0340.0000.0300.0310.0000.209
mnt_wines-0.025-0.2350.8300.0171.0000.5170.8240.5220.5050.5750.0540.7420.8230.805-0.3910.2350.1560.7450.9280.1150.0150.4070.1120.0940.3970.5180.3560.3010.0000.1540.2170.3030.267
mnt_fruits-0.020-0.0260.5820.0250.5171.0000.7140.7040.6910.570-0.1120.4730.6330.582-0.4440.0260.1290.5980.6830.0710.0290.3140.1210.0000.0690.2840.2590.0000.0000.1850.2680.1310.152
mnt_meat_products-0.014-0.1130.8170.0260.8240.7141.0000.7260.6980.640-0.0340.6830.8540.780-0.4940.1130.1550.7810.9400.0550.0310.3220.2260.0270.0970.3770.3100.0340.0000.2360.3490.1600.241
mnt_fish_products-0.030-0.0300.5770.0130.5220.7040.7261.0000.7000.565-0.1240.4660.6560.581-0.4600.0300.1310.6000.6950.0640.0510.3250.1400.0770.0100.2660.2690.0480.0000.1980.2860.1200.128
mnt_sweet_products-0.0330.0020.5670.0240.5050.6910.6980.7001.0000.541-0.1080.4620.6280.581-0.449-0.0020.1160.5740.6700.0690.0000.2940.1010.0000.0250.2670.2560.0470.0000.1730.2480.1170.111
mnt_gold_prods-0.041-0.0760.5060.0170.5750.5700.6400.5650.5411.0000.0900.5780.6490.540-0.2580.0760.2250.5070.6920.0650.0590.2700.0620.1250.0680.1810.1980.0850.0000.1200.1620.1120.158
num_deals_purchases-0.027-0.085-0.1960.0080.054-0.112-0.034-0.124-0.1080.0901.0000.284-0.0440.0970.3960.0850.217-0.323-0.0160.0000.0180.2120.3470.0000.0590.2450.1660.0000.0000.2700.3680.0770.098
num_web_purchases-0.025-0.1660.573-0.0020.7420.4730.6830.4660.4620.5780.2841.0000.6210.674-0.0970.1660.2010.5120.7290.0830.0360.2940.1610.0220.1590.1710.1670.0000.0000.0940.1490.1100.164
num_catalog_purchases-0.011-0.1790.7920.0290.8230.6330.8540.6560.6280.649-0.0440.6211.0000.707-0.5390.1790.1270.7530.8940.0650.0000.3870.1190.0880.1910.3600.3150.1110.0000.2010.2930.1900.219
num_store_purchases-0.022-0.1670.7320.0040.8050.5820.7800.5810.5810.5400.0970.6740.7071.000-0.4580.1670.1150.6770.8060.1070.0240.4030.0840.1790.2140.2280.1950.0810.0000.1370.2000.1360.149
num_web_visits_month-0.0120.134-0.644-0.019-0.391-0.444-0.494-0.460-0.449-0.2580.396-0.097-0.539-0.4581.000-0.1340.305-0.617-0.4760.0540.0000.3450.2160.0780.0000.3080.2040.0000.0000.2140.3240.0940.121
age-0.005-1.0000.2170.0170.2350.0260.1130.030-0.0020.0760.0850.1660.1790.167-0.1341.000-0.0150.1550.1590.1130.0910.2210.3070.0530.0520.0830.0530.0000.1380.1350.1820.0430.000
days_since_signup-0.0030.015-0.0230.0260.1560.1290.1550.1310.1160.2250.2170.2010.1270.1150.305-0.0151.0000.0020.1840.0450.0260.0390.0000.0070.0090.0000.0000.0000.0490.0270.0320.0160.201
income_per_member-0.007-0.1550.863-0.0010.7450.5980.7810.6000.5740.507-0.3230.5120.7530.677-0.6170.1550.0021.0000.8030.0590.1620.4100.2420.0000.2020.4050.3200.1180.0000.5060.4020.1930.303
total_spent-0.025-0.1590.8510.0190.9280.6830.9400.6950.6700.692-0.0160.7290.8940.806-0.4760.1590.1840.8031.0000.0930.0220.4380.2250.0560.2470.5260.4170.1520.0000.2210.3240.2660.293
education0.0000.1130.0540.0000.1150.0710.0550.0640.0690.0650.0000.0830.0650.1070.0540.1130.0450.0590.0931.0000.0000.0510.1050.0000.0530.0340.0320.0170.0390.0330.0330.0000.093
marital_status0.0000.0910.0000.0260.0150.0290.0310.0510.0000.0590.0180.0360.0000.0240.0000.0910.0260.1620.0220.0001.0000.0370.0760.0000.0000.0200.0290.0000.0000.3030.0440.0000.145
kidhome0.0000.2210.2860.0660.4070.3140.3220.3250.2940.2700.2120.2940.3870.4030.3450.2210.0390.4100.4380.0510.0371.0000.0520.0300.1620.2090.1840.0790.0280.5260.6210.1510.073
teenhome0.0000.3070.1740.0500.1120.1210.2260.1400.1010.0620.3470.1610.1190.0840.2160.3070.0000.2420.2250.1050.0760.0521.0000.0380.0240.2050.1460.0000.0000.4660.5630.0940.158
accepted_cmp30.0000.0530.0000.0430.0940.0000.0270.0770.0000.1250.0000.0220.0880.1790.0780.0530.0070.0000.0560.0000.0000.0300.0381.0000.0730.0740.0900.0610.0000.0000.0000.5660.251
accepted_cmp40.0000.0520.1390.0000.3970.0690.0970.0100.0250.0680.0590.1590.1910.2140.0000.0520.0090.2020.2470.0530.0000.1620.0240.0731.0000.3070.2380.2870.0000.0710.0820.6170.177
accepted_cmp50.0000.0830.4490.0000.5180.2840.3770.2660.2670.1810.2450.1710.3600.2280.3080.0830.0000.4050.5260.0340.0200.2090.2050.0740.3071.0000.4040.2140.0000.2420.3470.7370.320
accepted_cmp10.0450.0530.3620.0000.3560.2590.3100.2690.2560.1980.1660.1670.3150.1950.2040.0530.0000.3200.4170.0320.0290.1840.1460.0900.2380.4041.0000.1670.0000.1970.2780.6920.294
accepted_cmp20.0360.0000.0670.0340.3010.0000.0340.0480.0470.0850.0000.0000.1110.0810.0000.0000.0000.1180.1520.0170.0000.0790.0000.0610.2870.2140.1671.0000.0000.0580.0730.6690.162
complain0.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.0490.0000.0000.0390.0000.0280.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
n_family_members0.0000.1350.2290.0300.1540.1850.2360.1980.1730.1200.2700.0940.2010.1370.2140.1350.0270.5060.2210.0330.3030.5260.4660.0000.0710.2420.1970.0580.0001.0000.7620.1010.260
n_sons0.0000.1820.3040.0310.2170.2680.3490.2860.2480.1620.3680.1490.2930.2000.3240.1820.0320.4020.3240.0330.0440.6210.5630.0000.0820.3470.2780.0730.0000.7621.0000.1600.203
total_accepted0.0000.0430.2170.0000.3030.1310.1600.1200.1170.1120.0770.1100.1900.1360.0940.0430.0160.1930.2660.0000.0000.1510.0940.5660.6170.7370.6920.6690.0000.1010.1601.0000.426
response0.0340.0000.1920.2090.2670.1520.2410.1280.1110.1580.0980.1640.2190.1490.1210.0000.2010.3030.2930.0930.1450.0730.1580.2510.1770.3200.2940.1620.0000.2600.2030.4261.000

Missing values

2023-02-13T12:25:54.255184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T12:25:55.940398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idyear_birtheducationmarital_statusincomekidhometeenhomedt_customerrecencymnt_winesmnt_fruitsmnt_meat_productsmnt_fish_productsmnt_sweet_productsmnt_gold_prodsnum_deals_purchasesnum_web_purchasesnum_catalog_purchasesnum_store_purchasesnum_web_visits_monthaccepted_cmp3accepted_cmp4accepted_cmp5accepted_cmp1accepted_cmp2complainz_cost_contactz_revenueagen_family_membersn_sonsdays_since_signupincome_per_membertotal_spenttotal_acceptedresponse
055241957GraduationSingle58138.0002012-09-0458635885461728888381047000000311631066358138.000000161701
121741954GraduationSingle46344.0112014-03-0838111621621125000000311663211315448.0000002700
241411965GraduationTogether71613.0002013-08-2126426491271112142182104000000311552031235806.50000077600
361821984GraduationTogether26646.0102014-02-10261142010352204600000031136311398882.0000005300
453241981PhDMarried58293.0102014-01-19941734311846271555365000000311393116119431.00000042200
574461967MasterTogether62513.0012013-09-0916520429804214264106000000311533129320837.66666771600
69651971GraduationDivorced55635.0012012-11-13342356516450492747376000000311492159327817.50000059000
761771985PhDMarried33454.0102013-05-0832761056312324048000000311353141711151.33333316900
848551974PhDTogether30351.0102013-06-06191402433213029000000311463138810117.0000004601
958991950PhDTogether5648.0112014-03-13682806111311002010000031170421081412.0000004910
idyear_birtheducationmarital_statusincomekidhometeenhomedt_customerrecencymnt_winesmnt_fruitsmnt_meat_productsmnt_fish_productsmnt_sweet_productsmnt_gold_prodsnum_deals_purchasesnum_web_purchasesnum_catalog_purchasesnum_store_purchasesnum_web_visits_monthaccepted_cmp3accepted_cmp4accepted_cmp5accepted_cmp1accepted_cmp2complainz_cost_contactz_revenueagen_family_membersn_sonsdays_since_signupincome_per_membertotal_spenttotal_acceptedresponse
223070041984GraduationSingle11012.0102013-03-16822432671233312910000031136214705506.0000008410
223198171970MasterSingle44802.0002012-08-217185310143131020294128000000311501067744802.000000104900
223280801986GraduationSingle26816.0002012-08-175051634310034000000311341068126816.0000002200
223394321977GraduationTogether666666.0102013-06-0223914188112431360000003114331392222222.0000006200
223483721974GraduationMarried34421.0102013-07-018133762911027000000311463136311473.6666673000
2235108701967GraduationMarried61223.0012013-06-1346709431824211824729345000000311533138120407.666667134100
223640011946PhDTogether64014.0212014-06-10564060300087825700010031174531912802.80000044410
223772701981GraduationDivorced56981.0002014-01-259190848217321224123136010000311391015556981.000000124110
223882351956MasterTogether69245.0012014-01-24842830214803061265103000000311643115623081.66666784300
223994051954PhDMarried52869.0112012-10-154084361212133147000000311664262213217.25000017201